@inproceedings{913f2595f72241309330aef34d49d708,
title = "A dynamic load identification method using gru neural network",
abstract = "The Gated Recurrent Unit (GRU) neural network neural network is introduced into the identification of dynamic load. Using the {"}memory{"} characteristics of the GRU neural network combined with the solution principle of vibration response, a time-domain dynamic load identification method based on GRU neural network is proposed. Dynamic load identification experiments are carried out on a stiffened panel subjected to two stationary random point loads. The results show that the time histories of the loads can be accurately identified by using this method. At the same time, the power spectral density functions of the identified loads and the actual loads also have a high degree of coincidence. The proposed method does not need to establish the dynamic model of the structure, which provides an effective load identification approach for engineering structures.",
keywords = "Deep learning, GRU neural network, Load identification, Random dynamic load",
author = "Zaifei Kang and Te Yang and Shuya Liang and Zhichun Yang",
note = "Publisher Copyright: {\textcopyright} {"}Advances in Acoustics, Noise and Vibration - 2021{"} Proceedings of the 27th International Congress on Sound and Vibration, ICSV 2021. All rights reserved.; 27th International Congress on Sound and Vibration, ICSV 2021 ; Conference date: 11-07-2021 Through 16-07-2021",
year = "2021",
language = "英语",
series = "{"}Advances in Acoustics, Noise and Vibration - 2021{"} Proceedings of the 27th International Congress on Sound and Vibration, ICSV 2021",
publisher = "Silesian University Press",
editor = "Eleonora Carletti and Malcolm Crocker and Marek Pawelczyk and Jiri Tuma",
booktitle = "{"}Advances in Acoustics, Noise and Vibration - 2021{"} Proceedings of the 27th International Congress on Sound and Vibration, ICSV 2021",
}